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使用ABAQUS软件进行过渡网格划分，帮助用户建立良好的过渡网格的小工具(use the ABAQUS to divide the transition gridding)

Echo cancellation using NLMS adaptive filter

柔顺机构的拓扑优化设计的代码，考虑输入输出(Topology Optimization of Compliant Mechanisms designed code, consider the input and output)

设T 是一棵带权树，树的每一条边带一个正权。又设S 是T 的顶点集,T/S 是从树T 中 将S中顶点删去后得到的森林。如果T/S中所有树的从根到叶的路长都不超过d ，则称T/S 是一个d 森林。(this is a project )

基于SVM的数据分类，通过IRIS数据进行验证，效果分类准确(SVM-based data classification, through the IRIS data validation, classification accuracy results)

粉土的阻尼比拟合程序。拟合模型选择Davidenkov模型。(The silt damping match the proper procedure. The Fitting Model Davidenkov model.)

说明：  光流场HS算法，主要用的是差分算法，MATLAB编写(Optical flow field HS algorithm, the main differential algorithm)

％ 奇异值分解 (sigular value decomposition,SVD) 是另一种正交矩阵分解法；SVD是最可靠的分解法， ％ 但是它比QR 分解法要花上近十倍的计算时间。[U,S,V]=svd(A)，其中U和V代表二个相互正交矩阵， ％ 而S代表一对角矩阵。 和QR分解法相同者， 原矩阵A不必为正方矩阵。 ％ 使用SVD分解法的用途是解最小平方误差法和数据压缩。用svd分解法解线性方程组，在Quke2中就用这个来计算图形信息，性能相当的好。在计算线性方程组时，一些不能分解的矩阵或者严重病态矩阵的线性方程都能很好的得到解( Singular value decomposition (sigular value decomposition, SVD) is another orthogonal matrix decomposition method SVD decomposition is the most reliable method, but it takes more than QR decomposition near ten times the computing time. [U, S, V] = svd (A), in which U and V on behalf of two mutually orthogonal matrix, and the S on behalf of a diagonal matrix. And QR decomposition are the same, the original matrix A is no need for the square matrix. The use of SVD decomposition method are used as a solution of least squares error method and data compression. Using SVD decomposition solution of linear equations, in Quke2 on to use this information to calculate the graphics performance quite good. In the calculation of linear equations, some indecomposable matrix or serious pathological matrix of linear equations can be a very good solution)

采用有限元离散计算一维声子、光子能带，参数见文件源码内(Calculated using the finite element discrete one-dimensional phonon, photon energy band, within the parameters see document source)

用Matlab程序求解计算物理中使用到的方程，包括：薛定谔方程、傅立叶变换、波函数求解等等(Matlab file is used to describe and solve the equations in the computational physics, including, Schrodinger equation, Fourier transform, wave function solution, etc.)